Cathodic sputtering is widely used for depositing thin layers or films of materials from sputter targets onto desired substrates such as semiconductor wafers. Basically, a cathode assembly including a sputter target is placed together with an anode in a chamber filled with an inert gas, preferably argon. The desired substrate is positioned in the chamber near the anode with a receiving surface oriented normally to a path between the cathode assembly and the anode. A high voltage electric field is applied across the cathode assembly and the anode.
Electrons ejected from the cathode assembly ionize the inert gas. The electrical field then propels positively charged ions of the inert gas against a sputtering surface of the sputter target. Material dislodged from the sputter target by the ion bombardment traverses the chamber and deposits on the receiving surface of the substrate to form the thin layer or film.
One factor affecting the quality of the layer or film produced by a sputtering process is the “cleanliness” of the material from which the sputter target is made. Since the cleanliness of the material from which a sputter target is made affects the quality of layers or films produced using that target, it is obviously desirable to use relatively clean materials in fabricating sputter targets.
However, since the target material consumption during the sputtering process is highly non-uniform (this is especially true for modern sputter-deposition tools with planar magnetron sources using a specifically-confined non-uniform magnetic fields formed by rotating or stationary magnets), localized highly eroded regions or “sputter tracks” typically form on the surface of the target. Although the sputter track region of the target is heavily eroded, other regions of the target experience significantly less erosion or even remain practically unsputtered. Due to this difference in erosion intensity, there is a substantial difference in the contribution of different target regions to the sputtering process. While the material cleanliness of the heavily-eroded sputter track region is absolutely critical for the film quality, the cleanliness of the regions where insignificant sputtering or no sputtering occurs is less critical.
As presently understood, prior art cleanliness criteria did not distinguish the “sputter track” and “non-sputter track” regions of the target surface. Thus, prior art techniques for characterizing sputter targets did not take into account the possibility that, in a field where a few or even one single minute flaw can impact on a decision to accept or reject a target, identifying flaw size and determining whether flaws are located inside or outside the sputter track regions can improve target production yield without compromising the functional target quality.
For example, FIG. 1 illustrates a technique for characterizing aluminum and aluminum alloy sputter targets similar to the methods proposed in Leroy et al. U.S. Pat. Nos. 5,887,481 and 5,955,673. The technique illustrated in FIG. 1 employs a pulse-echo method performed on a test sample 10 having a planar upper surface 12 and a parallel planar lower surface 14. In accordance with this technique, focused ultrasonic transducer 16 irradiates each of a sequence of positions on the upper surface 12 of the test sample 10 with a single, short-duration, high-frequency ultrasound pulse 18 having a frequency of at least 5 MHz, and preferably 10-50 MHz. The ultrasonic transducer 16 then switches to a sensing mode and detects a series of echoes 20 induced by the ultrasound pulse 18.
One factor which contributes to these echoes 20 is scattering of sonic energy from the ultrasound pulse 18 by flaws 22 in the test sample 10. By comparing the amplitudes of echoes 20 induced in the test sample 10 with the amplitudes of echoes induced in reference samples (not shown) having compositions similar to that of the test sample 10 and blind, flat-bottomed holes of fixed depth and diameter, it is possible to detect and count flaws 22 in the test sample 10.
The number of flaws detected by the technique of FIG. 1 has to be normalized in order to facilitate comparison between test samples of different size and geometry. Conventionally, the number of flaws is normalized by volume, that is, the sputter target materials are characterized in units of “flaws per cubic centimeter.” The volume associated with the echoes 20 from each irradiation of the test sample 10 is determined, in part, by estimating an effective cross-section of the pulse 18 in the test sample 10.
A number of factors detract from the ability of the transducer 16 to detect sonic energy scattered by the flaws 22. This reduces the sensitivity of the technique.
One such factor is relative weakness of the scattered energy. A portion of the scattered energy is attenuated by the material making up the test sample 10. Furthermore, since the flaw sizes of interest, which range from approximately 0.04 mm to 1 mm, are of same order or less than the wavelength of ultrasound in metals (for example, the wavelength of sound in aluminum for the frequency range of 10 MHz is 0.6 mm), the pulse 18 has a tendency to refract around the flaws 22, which reduces the scattering intensity.
Another factor detracting from the ability of the transducer 16 to detect the sonic energy scattered by the flaws 22 is the noise generated by scattering of the pulse 18 at the boundaries between grains having different textures. In fact, the texture-related noise can be so great for high-purity aluminum having grain sizes on the order of several millimeters that small flaws within a size range of approximately 0.05 mm to 0.4 mm and less cannot be detected. Larger grain sizes reduce the signal-to-noise ratio for the sonic energy scattered by the flaws when compared to the noise induced by the grain boundaries.
Other factors affecting the sensitivity and resolution of the technique of FIG. 1 includes the pulse frequency, duration and waveform; the degree of beam focus and the focal spot size; the coupling conditions, that is, the efficiency with which the sonic energy travels from the transducer 16 to the test sample 10; and the data acquisition system parameters.
Another drawback to the technique of FIG. 1 is that the calculation of the “flaws per cubic centimeter” in the test sample 10 presupposes that only flaws 22 within a determinable cross-sectional area scatter sonic energy back toward the transducer 16. In fact, the pulse 18, due to its wave nature, does not have localized, well-determined boundaries.
The distribution of the energy of the pulse 18 within the test sample 10, under simplifying assumptions, permits one to define a corridor 30 having a determinable cross-section beneath the transducer 16 in which most of the energy should be concentrated. Nevertheless, some of the energy of the pulse 18 will propagate outside this corridor 30. As a result, the transducer may detect sonic energy scattered by relatively large flaws 22 located outside the estimated corridor 30, thereby overestimating the density of flaws 22 in the test sample 10 and underestimating their sizes. Because of this, material cleanliness characteristics become to some degree uncertain.
Another drawback to the technique of FIG. 1 is inability to determine the proximity of a flaw to the sputter track region. This increases the risk that a manufacturer will accept targets which are undesirable because of defects located at or near the sputter track regions. Alternatively, it increases the risk that the manufacturer will reject potentially useful targets to compensate for the risk of accepting targets having unacceptable defects in or near the sputter track region.
Thus, there remains a need in the art for non-destructive techniques for characterizing sputter target materials having greater sensitivity than the method illustrated in FIG. 1. There also remains a need for techniques which permit the comparison of the cleanliness of different sputter target materials in a manner which is not dependent on arbitrary volumetric estimations in the form of “flaws per cubic unit.”
Partially, these drawbacks and limitations have been overcome by the prior art technique suggested in Tosoh SMD International Application No. PCT/US99/13066. Application PCT/US99/13066 discloses a method which overcomes most, though not necessarily all, of the disadvantages stated above. Since the data collection, analysis and imaging techniques proposed in Application PCT/US99/13066 are intended to detect, identify, and count flaws with sizes in the range of 0.04 mm to 0.1 mm (that is, flaws having relative sizes less than the size of the single pixel of the data acquisition and displaying device), each single flaw is represented by the single data point (pixel) with a value equal to the signal amplitude.
The technique according to Application No. PCT/US99/13066 counts the total number of flaw data points or pixels “CF” to quantify the degree of target material cleanliness. FIG. 2 illustrates this method for characterizing the cleanliness of sputter target material. In accordance with this method, a cylindrical sample 50 of the sputter target material is compressed or worked to produce a disc-shaped test sample 52 having a planar upper surface 54 and a substantially parallel planar lower surface 56. Thereafter, a focused ultrasonic transducer 60 is positioned near the upper surface 54. The transducer 60 irradiates the upper surface 54 of the test sample 52 with a single, short-duration, megahertz-frequency-range ultrasonic pulse 62. The transducer 60 subsequently detects an echo 64 induced in the test sample 52 by the pulse 62. The transducer 60 converts the echo 64 into an electrical signal (not shown), which is processed for use in characterizing the test sample 52.
As illustrated in FIG. 3, the test sample 52 first is immersed in deionized water (not shown) in a conventional immersion tank 80. The transducer 60 is mounted on a mechanical X-Y scanner 82 in electrical communication with a controller 84, such as a PC controller. The controller 84 is programmed in a conventional manner to induce the mechanical X-Y scanning unit 82 to move the transducer 60 in a raster-like stepwise motion across the upper surface 54 of the test sample 52.
The transducer 60 is oriented so that the pulse 62 propagates through the deionized water (not shown) in the immersion tank 80 and strikes the test sample 52 approximately normally to the upper surface 54. The transducer 60 preferably is spaced from the upper surface 54 such that the pulse 62 is focused on a zone 86 (FIG. 2) of the test sample 52.
An echo acquisition system for use in the method of FIGS. 2 and 3 includes a low noise gated preamplifier 90 and a low noise linear amplifier 92 with a set of calibrated attenuators. When sufficient time has elapsed for the echoes to arrive at the transducer 60, the controller 84 switches the transducer 60 from a transmitting mode to a gated electronic receiving mode. The echoes 64 are received by the transducer 60 and converted into an RF electric amplitude signal (not shown). The amplitude signal is amplified by the preamplifier 90 and by the low noise linear amplifier 92 to produce a modified amplitude signal. The attenuators (not shown) associated with the low noise linear amplifier 92 attenuate a portion of the texture-related noise. The modified amplitude signal then is digitized by the analog-to-digital converter 94 before moving on to the controller 84. The analog-to-digital conversion is performed so as to preserve amplitude information from the analog modified amplitude signal.
Flaws of given sizes are detected by comparing the digitized modified amplitude signals obtained from the sample 52 with reference values derived from tests conducted on reference samples (not shown) having compositions similar to that of the test sample 52 and blind, flat-bottomed holes of fixed depth and diameter.
The PC controller 84 includes a microprocessor 100 programmed to control the data acquisition process. The microprocessor 100 also is programmed to calculate the cleanliness factor characterizing the material of the samples 50, 52. It discriminates the texture-related backscattering noise and distinguishes “flaw data points,” that is, data points where comparison of the digitized, modified amplitude signals with the reference values indicate the presence of flaws. The microprocessor 100 maintains a count of the flaw data points detected during the testing of a test sample 52 to determine the “flaw count” CF. The microprocessor 100 also is programmed to distinguish “no-flaw data points,” that is, data points where comparison of the digitized, modified amplitude signals with the calibration values indicates the absence of flaws.
The microprocessor 100 also determines a total number of data points “CDP,” that is, the sum of the flaw count CF and the number of no-flaw data points. Although the total number of data points could be determined by adding the counts of the flaw data points and the no-flaw data points, it preferably is determined by counting the total number of positions “C1” along the upper surface 54 at which the test sample 52 is irradiated by the transducer 60 and subtracting the number of digitized RF signals “CN” which the data acquisition circuitry was unable, due to noise or other causes, to identify as either flaw data points or no-flaw data points. Having determined the flaw count CF and the total number of data points CDP, the microprocessor is programmed to calculate a cleanliness factor FC=(CF/CDP)×106 to characterize the material comprising the samples 50, 52.
Another way in which the method of FIG. 2 characterizes the material comprising the samples 50, 52 is by determining the size distribution of flaws in the test sample 52. More specifically, the method characterizes the cleanliness of the sample 52 by defining amplitude bands or ranges; comparing the amplitudes of the digitized, modified amplitude signals with the amplitude bands so as to form subsets of the modified amplitude signals; counting the data points in these subsets of modified amplitude signals to determine a modified amplitude signal count for each amplitude band; and constructing a histogram relating the modified signal counts to said plurality of amplitude bands. Since the amplitudes represented by the digitized, modified amplitude signals are related to the sizes of flaws detected in the sample 52, the histogram provides an indication of the flaw size distribution in the sample 52.
However, it has to be taken into consideration that a single flaw with size larger than about 0.1 mm may not be represented by one single data point. For example, a single flaw larger than about 0.1 mm may exceed the effective cross-section of a single pulse. This possibility makes it more difficult or even impossible to determine the total number of flaws based on the raw count of flaw data points or pixels.
Another drawback to the methods of FIGS. 1-3 is that they are designed to test and characterize one target or sample (not shown) at a time. That is, the methods as proposed in the references appear to have been designed to conduct each test sequentially and without overlap even when a queue of targets or samples (not shown) becomes available for testing in the course of a manufacturing process.
Thus, there remains a need in the art for non-destructive techniques to characterize cleanliness of sputtering target which are able to identify and properly count the flaws with greater range of flaw sizes and to provide separate flaw counts for sputter track and non-sputter track regions.